Managing a smart city

ABSTRACT

A smart city management system may enable creating a digital twin of the smart city based on mapping lidar data for the smart city and radio frequency data for the smart city; determining placement of a set of network devices in the smart city based on the created digital twin; and providing a visualization of the determined placement of the set of network devices.

CROSS-REFERENCE TO OTHER PATENT APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 17/151,241, which has been incorporated byreference herein in its entirety. The U.S. patent application Ser. No.17/151,241 is a continuation-in-part of U.S. patent application Ser. No.17/009,759, which has been incorporated by reference herein in itsentirety.

BACKGROUND

The collection of data about environments and geographic areas isbecoming increasingly important as people and organizations try tounderstand the physical and technological entities in those environmentsand geographic areas. This is even more salient when trying to installor deploy a smart city. Universal mechanisms and standards for such aninstallation or deployment do not exist, and an installation ordeployment generally requires the cooperation and use of numerousvarious entities providing different products and services. Determininguseful information about environments and geographic areas in order tooptimize the deployment or installation of network resources for a smartcity can be incredibly difficult, given the increasing amount of datafrom disparate data sources about these environments and geographicareas.

BRIEF DESCRIPTION OF THE DRAWINGS

The following detailed description references the drawings, wherein:

FIG. 1 is a block diagram depicting an example environment in whichvarious examples may be implemented as a smart city system.

FIG. 2A is a diagram depicting an example user interface for managing asmart city.

FIG. 2B is a diagram depicting an example user interface for managing asmart city.

FIG. 2C is a diagram depicting an example user interface for managing asmart city.

FIG. 2D is a diagram depicting an example user interface for managing asmart city.

FIG. 3 is a block diagram depicting an example machine-readable storagemedium comprising instructions executable by a processor for managing asmart city.

FIG. 4 is a flow diagram depicting an example method for managing asmart city.

FIG. 5 is a flow diagram depicting an example method for managing asmart city.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts. Itis to be expressly understood, however, that the drawings are for thepurpose of illustration and description only. While several examples aredescribed in this document, modifications, adaptations, and otherimplementations are possible. Accordingly, the following detaileddescription does not limit the disclosed examples. Instead, the properscope of the disclosed examples may be defined by the appended claims.

The collection of data about environments and geographic areas isbecoming increasingly important as people and organizations try tounderstand the physical and technological entities in those environmentsand geographic areas. This is even more salient when trying to installor deploy a smart city. Universal mechanisms and standards for such aninstallation or deployment do not exist, and an installation ordeployment generally requires the cooperation and use of numerousvarious entities providing different products and services. Determininguseful information about environments and geographic areas in order tooptimize the deployment or installation of network resources for a smartcity can be incredibly difficult, given the increasing amount of datafrom disparate data sources about these environments and geographicareas.

Examples disclosed herein provide technical solutions to these technicalchallenges by managing smart cities in an automated way that enablesinstallment, development, visualizations, analytics, and recommendationsrelated to the smart city. The solutions described herein enable animproved and effective analysis and presentation of a complicated, largeset of data related to managing a smart city. Further, by collecting andanalyzing information about both the physical and technological entitiesabout the geographic areas of the smart city, the technical solutionsdisclosed herein also enable optimization of the physical and especiallytechnological entities in the smart city in a myriad of ways.

Some examples disclosed herein to manage smart cities include creating adigital twin of the smart city based on mapping lidar and imagery datafor the smart city and radio frequency data for the smart city;determining placement of a set of network devices in the smart citybased on the created digital twin; and providing a visualization of thedetermined placement of the set of network devices.

Some of the examples disclosed herein to manage smart cities enablemapping lidar and imagery data and radio frequency data for the smartcity; identifying a set of existing network devices in the smart citybased on the mapped lidar and radio frequency data; determiningplacement of a set of network devices in the smart city, where the setof network devices comprises the set of existing network devices and aset of new network devices; and providing a recommendation of thedetermined placement of the set of network devices.

Some examples disclosed herein to manage smart cities enableinstructions to map lidar and imagery data and radio frequency data forthe smart city; instructions to identify a set of existing networkdevices in the city based on the mapped lidar and radio frequency data;instructions to determine placement of a set of network devices in thesmart city, where the set of network devices comprises the set ofexisting network devices and a set of new network devices; andinstructions to provide a recommendation of the determined placement ofthe set of network devices

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a,” “an,” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. The term“plurality,” as used herein, is defined as two or more than two. Theterm “another,” as used herein, is defined as at least a second or more.The term “coupled,” as used herein, is defined as connected, whetherdirectly without any intervening elements or indirectly with at leastone intervening elements, unless otherwise indicated. Two elements canbe coupled mechanically, electrically, or communicatively linked througha communication channel, pathway, network, or system. The term “and/or”as used herein refers to and encompasses any and all possiblecombinations of one or more of the associated listed items. It will alsobe understood that, although the terms first, second, third, etc. may beused herein to describe various elements, these elements should not belimited by these terms, as these terms are only used to distinguish oneelement from another unless stated otherwise or the context indicatesotherwise. As used herein, the term “includes” means includes but notlimited to, the term “including” means including but not limited to. Theterm “based on” means based at least in part on.

FIG. 1 is an example environment 100 in which various examples may beimplemented as a smart city management system 110. In some examples,environment 100 may include various components including servercomputing device 130 and mobile devices 140 (illustrated as 140A, 140B,. . . , 140N). Each client computing device 140A, 140B, . . . , 140N maycommunicate requests to and/or receive responses from server computingdevice 130. Server computing device 130 may receive and/or respond torequests from mobile devices 140. Mobile devices 140 may be any type ofmobile computing device capable of sending and/or receiving data toserver computing device 130. For example, mobile devices 140 may includea laptop computing device, an all-in-one computing device, a thinclient, a workstation, a tablet computing device, a mobile phone, anelectronic book reader, a network-enabled appliance such as a “Smart”speaker, a network-connected radio, a software defined radio, widebandtuner, and/or other electronic device suitable for collecting data andtransmitting that data to the server computing device 130. While servercomputing device 130 is depicted as a single computing device, servercomputing device 130 may include any number of integrated or distributedcomputing devices serving at least one software application forconsumption by mobile devices 140. Data store 129 can be anynon-transitory machine-readable storage. In some examples, data store129 can comprise a Solid State Drive (SSD), Hard Disk Drive (HDD), adatabase, a networked database storage system, a cloud storage, and/orother type of data store that stores information related to smart citymanagement system 110.

The various components (e.g., components 129, 130, and/or 140) depictedin FIG. 1 may be coupled to at least one other component via a network50. Network 50 may comprise any infrastructure or combination ofinfrastructures that enable electronic communication between thecomponents. For example, network 50 may include at least one of theInternet, an intranet, a PAN (Personal Area Network), a LAN (Local AreaNetwork), a WAN (Wide Area Network), a SAN (Storage Area Network), a MAN(Metropolitan Area Network), a wireless network, a cellularcommunications network, a Public Switched Telephone Network, and/oranother network.

According to various implementations, smart city management system 110and the various components described herein may be implemented inhardware and/or a combination of hardware and programming thatconfigures hardware. Furthermore, in FIG. 1 and other Figures describedherein, different numbers of components or entities than depicted may beused.

Smart city management system 110 may comprise a smart city datamanagement engine 121, a network device placement engine 122, avisualization engine 123, and/or other engines. The term “engine”, asused herein, refers to a combination of hardware and programming thatperforms a designated function. As is illustrated respect to FIG. 3, thehardware of each engine, for example, may include one or both of aprocessor and a machine-readable storage medium, while the programmingis instructions or code stored on the machine-readable storage mediumand executable by the processor to perform the designated function.

Smart city data management engine 121 may manage data related to a smartcity. A smart city may refer to a geographic area, comprising amunicipality, city, town, village, and/or other geographically boundarea. In some examples, a smart city may be mapped into one or morezones, where each zone includes a sub-portion of the smart citygeographically. In some examples, zones may further be broken up intosub-zones, etc., to better enable management of network devices in asmart city at a more granular level.

A smart city may have millions and/or even billions of wireless devices(e.g., mobile phones, smart phones, tablets, “smart” devices, internetof things (IoT) devices, and/or other devices that have wirelesscapabilities) operating therein. As such, a smart city may need to havea substantial amount of network devices (e.g., switches, routers, accesspoints, gateways, cameras, sensors, IoT sensors, and/or other networkdevices that enable propagation and management of wireless signals,wireless devices, and/or smart city management) at appropriate locationsin order to enable and manage the usage of the wireless devicesoperating therein.

Smart city data management engine 121 may map lidar and imagery data andradio frequency data for the smart city. For example, the lidar data andradio frequency data may be mapped to each other, based on GlobalPositioning System (GPS) coordinates. The smart city data managementengine 121 may map the lidar data and radio frequency data in a manner,that is the same as or similar to the functionality described in U.S.patent application Ser. No. 17/009,759 (which is incorporated herein byreference). In some examples, the smart city data management engine 121may also map imagery data with the mapped lidar and radio frequency datain a manner, that is the same as or similar to the functionalitydescribed in U.S. patent application Ser. No. 17/009,759.

In some examples, the smart city data management engine 121 may create adigital twin of the smart city based on the mapped lidar and imagerydata for the smart city and radio frequency data for the smart city. Adigital twin may comprise, for example, a digital representation of thephysical and wireless environment of the smart city. In some examples,smart city data management engine 121 may create the digital twin byalso mapping the lidar and imagery data and radio frequency data withimagery data for the smart city. In some examples, the smart city datamanagement engine 121 may also identify existing network devices and/orexisting wireless devices in the smart city based on wireless signaturesbeing emitted by the existing network devices and/or wireless devices.As such, the digital twin created by the smart city data managementengine 121 may include lidar data, radio frequency data, imagery data,wireless device data, network device data, any combination thereof,and/or other data related to the smart city.

The smart city data management engine 121 may also manage and/or updatethe digital twin of the smart city. For example, the smart city datamanagement engine 121 may periodically update some or all of the dataused to create the digital twin. In some examples, the smart city datamanagement engine 121 may automatically update the digital twin atpredetermined intervals, upon a request to deploy an additional and/ornew network device, upon detecting a change in existing wireless and/ornetwork devices, upon new data being received by the smart citymanagement system 110, upon a request received from a user of the smartcity management system 110, upon a request received from another system,and/or for other reasons.

In some examples, the smart city data management engine 121 may create,manage, and/or update digital twins for each zone of the smart city. Insome examples, the smart city data management engine 121 may maintain,manage, and/or update merely lidar and imagery data and radio frequencydata for the smart city and/or each of its zones.

Upon collection of this data for the smart city and/or each of itszones, smart city data management engine 121 may store the collecteddata in data storage 129.

Network device placement engine 122 may determine placement of a set ofnetwork devices in the smart city. In some examples, network deviceplacement engine 122 may determine placement of the set of networkdevices in the smart city based on the mapped lidar and radio frequencydata, the created digital twin, and/or any other data related to thesmart city.

In some examples, the network device placement engine 122 may determinethe set of network devices to place in the smart city. In some examples,the set of network devices may include the existing set of networkdevices in the smart city and a new set of network devices, or may justinclude a new set of network devices to place in the smart city.

The network device placement engine 122 may determine the set of networkdevices based on information received about a required set ofcapabilities in each zone of the set of zones in the smart city. Forexample, the network device placement engine 122 may determine amatching set of network devices that provide the required set ofcapabilities. The network device placement engine 122 may also determinethe geographical positions at which to place the set of network devicesbased on one or more factors, including the required set of capabilitiesand specifications (e.g., coverage, dimension, size, frequency,functionality, compatibility, etc.) of each matching network device,physical infrastructure of a location in which the network device is tobe placed, wireless infrastructure of the location, the mapped lidar andradio frequency data of the location, imagery data for the location, theexisting network devices in the location, the existing wireless devicesin the location, any combination thereof, and/or other data related tothe location.

“Location,” as used herein, may comprise the smart city, one or multiplezones in the smart city, one or multiple sub-zones, one or multipleblocks of the city, an area defined by a set of longitudinal andlatitudinal coordinates in the city, and/or another geographic area ofthe city.

“Physical infrastructure,” as used herein, may include physical objects,physical terrain, physical buildings, roads, bridges, walls, fences,signage, utility poles, and/or other physical attributes of a location.“Wireless infrastructure,” as used herein, may include energy and/orwireless signals emanating in wireless frequencies across the RadioFrequency spectrum. Wireless infrastructure may correlate to physicalinfrastructure in that physical objects (antennas, phones, WiFi routers,wireless cameras, laptops, etc) propagate the wireless signals.

As mentioned above, in some examples, the network device placementengine 122 may identify existing network devices in the location. Insome of these examples, the network device placement engine 122 maydetermine where to place a set of new network devices based on theplacement of existing network devices in the smart city. For example,the network device placement engine 122 may determine a set ofcapabilities required in the location that are not met by the existingnetwork devices, and may determine a matching set of new network devicesthat provide the required set of capabilities. The network deviceplacement engine 122 may determine the geographical positions at whichto place the set of new network devices based on one or more factors,including the required set of capabilities and specifications (e.g.,coverage, dimension, size, frequency, functionality, compatibility,etc.) of each matching network device, physical infrastructure of alocation in which the network device is to be placed, wirelessinfrastructure of the location, the mapped lidar and radio frequencydata of the location, imagery data for the location, the existingnetwork devices in the location, the existing wireless devices in thelocation, any combination thereof, and/or other data related to thelocation.

In some of these examples, the network device placement engine 122 mayalso consider the signal information from the set of existing networkdevices in order to determine the signal types and/or functionalitiesneeded in the location. Signal information may comprise, for example,mobile network data, frequency or channel, network type, networkprotocol, signal strength, coverage area, cellID, timing advance,Wifi/BlueTooth: Band, SSID, MAC, RSSI signal strength, authenticationmode, coverage area, baud rate, modulation type, data on futuredevelopments on additional spectrum like wideband spectrum (frequency orchannel, signal strength, coverage area, etc.), and/or other datarelated to a wireless signal. A wireless signature may comprise datafrom the signal information that could be used to identify a wirelessdevice and/or manufacturer of a wireless device.

In some of these examples, the network device placement engine 122 mayalso consider the wireless signatures available in the location toidentify the wireless devices and/or network devices that may already bepresent in the network. In these examples, the network device placementengine 122 may proactively de-conflict signals from the set of existingnetwork devices, the planned new set of network devices, other existingwireless devices in the location, and/or any combination thereof. Forexample, the network device placement engine 122 may identify wirelesssignals related to 2G, 3G, 4G, and/or 5G wireless communication in thelocation and may de-conflict signals from existing network devices andplanned new network devices in the location. In these examples, thenetwork device placement engine 122 may identify what frequencies arecurrently in use in a location. By doing so, the network deviceplacement engine 122 may identify potential gaps in the spectrum offrequencies used and may identify unused frequencies that are stillavailable for us. The identification of available frequencies can beincreasingly important for network devices that use Wi-Fi or BlueTooth,as the usage of these devices are multiplying at a rapid rate. Such anidentification also may enable the driving of regulatory conversationswhen applying for licenses in the VHF/UHF/Microwave/MMwave bands.

The network device placement engine 122 may also consider the networkspectrum available in the location to determine the set of new networkdevices to place in the location. For example, the network deviceplacement engine 122 may consider what frequency bands are being usedand select network devices and frequencies that do not conflict with theused frequency bands in the spectrum.

In some examples, the network device placement engine 122 may identifyexisting physical and/or wireless infrastructure based on the mappedlidar and radio frequency data and the wireless signatures determined ina location. In some examples, the network device placement engine 122may also consider imagery data mapped to the lidar and radio frequencydata for the location. The network device placement engine 122 mayperform asset extraction on key features in the location based on theidentified existing physical and wireless infrastructure. For example,the network device placement engine 122 may identify utility poles formounting network devices such as emitters or sensors, or manhole coversto identify existing fiber technology. The network device placementengine 122 may also identify physical objects and/or terrain in thelocation that could cause signal obstructions using thethree-dimensional lidar, radio frequency, imagery, and/or other data inthe data store 129. Based on that identification, the network deviceplacement engine 122 may optimize placement of new network devices toreduce signal obstruction, signal conflicts, and/or other issues thatcould cause a degradation or less optimal usage of the network devices.In some examples, the network device placement engine 122 may alsoidentify specific network gaps in the wireless environment of thelocation, may identify the existing strongest signals with the best datathroughput at the location, may determine where to place a networkdevice to obtain the strongest signals with the best data throughput atthe location, may determine what existing network devices to remove fordeconfliction and to avoid interference and congestion, and/or mayotherwise determine type and placement of a new network device in thelocation to optimize a smart city infrastructure.

As such, with the network device placement engine 122, de-confliction ofsignals is based on ground truth instead of mere theoretical modeling.Further, with the span of signal from 5G, de-conflicting signals fromall of the different devices and different compatibilities on differentfrequencies with the amount and scale of existing wireless and existingnetwork devices may be daunting or even impossible without an automatedcapability like that provided by the network device placement engine122.

By considering this de-confliction along with the physical terrain ofthe location, the capabilities required or requested in the location,the mapped lidar and radio frequency data, imagery data for thelocation, the existing wireless devices, the existing network devices,any combination thereof, and/or other information related to thelocation, the network device placement engine 122 may provide anoptimized placement of the new set of network devices for the location.

In some examples, network device placement engine 122 may continuallyupdate the placement of existing and new network devices atpredetermined, random, and/or user-initiated intervals.

The network device placement engine 122 may store information related tothe determined and updated placements, and the data used to make theplacements, along with timestamps and other related data in data storage129. In some examples, along with each stored placement of networkdevices, network device placement engine 122 may also store, for eachexisting and/or new network device, a device name, device ID, devicelocation data, network information data, network protocol data, signalstrength data, SSID, MCC/MNC data, LAC/TAC data, CellID data, Band data,ARFCN data for GSM, EARFCN data for LTE, RSSNR data, RSRQ data, ENBRNCdata, LCID data, baud rate, modulation type, unit data, latitude data,longitude data, speed data, timestamp, capabilities data, and/or otherinformation related to the network device. The network device placementengine 122 may use the information to update the placement of existingand new network devices.

In some examples, network device placement engine 122 may use the datarelated to the smart city to make predictions about a network event thatcould occur. For example, the network device placement engine 122 mayanalyze the data related to a location over time to determine adegradation of signal strength in a sub-area of the geolocation, a lossof a network device, a need for additional network device(s) to improvesignal strength, a drop in network coverage, a new signal that has beencollected, a new physical object that has been detected, or some othertype of network event has occurred. In some examples, the network eventmay comprise the prediction of signal strength at a given location at afuture point in time. In some examples, the network event may comprise adifference between expected network coverage and existing signalcoverage exceeding a predetermined threshold, based on collected radiofrequency data related to signal strength. The network device placementengine 122 may use the predications to determine and/or update theplacement of existing and new network devices.

In some of these examples, network device placement engine 122 mayprovide an alert related to the predicted network event. For example,the network device placement engine 122 may provide an alert that anetwork event may occur at a determined time interval, or may provideinformation about the network event.

In some of these examples, network device placement engine 122 mayanalyze the data related to the smart city over time to provide a planfor maintenance and upgrades of the wireless network(s) in the smartcity.

In some of these examples, network device placement engine 122 mayanalyze the data related to the smart city to plan spectrum usage in thesmart city.

In some of these examples, network device placement engine 122 mayanalyze the data related to the smart city to provide feedback to atelecommunications provider on deconfliction of frequency bands inlicensed and unlicensed space based on the measured signal strength andphysical terrain information included in the related data.

In some examples, network device placement engine 122, and smart citymanagement system 110 as a whole, may provide ground truth data totelecommunication providers about the smart city. Smart city managementsystem 110 may provide recommendations related to positions of networkdevices and plans for maintenance and upgrades of the telecommunicationnetwork provider's network in the smart city. In some of these examples,telecommunications providers that provide high radio frequency networks(e.g., 5G, 6G, and/or other high radio frequency networks) may use smartcity management system 110 for a better implementation and roll out oftheir high radio frequency network.

Returning to FIG. 1, to enable such planning and management,visualization engine 123 may provide a visualization of the smart city,or a location, with a recommended placement of each of the required setof devices.

In some examples, the visualization engine 123 may provide a map of thedetermined placement of the set of network devices in the smart city. Insome examples, the map may be an interactive map, and the visualizationengine 123 may enable the zooming in and/or out of the interactive mapinto a specific zone, sub-zone, location, etc., to get a closer orbroader look at the determined placements. The visualization engine 123may also depict the stored data about the smart city on the map. Forexample, the visualization engine 123 may depict the lidar data, radiofrequency data, imagery data, wireless signal data, physical terrain,city infrastructure, existing network devices, and/or other data relatedto the smart city. In some of these examples, the visualization engine123 may enable the selection of one or multiple types of the datarelated to the smarty city to be shown as overlaid on the section of themap being viewed.

The visualization engine 123 may also enable the selection of one ormultiple data points in the map. Responsive to receiving the selection,the visualization engine 123 may show additional data related to the oneor multiple data points. For example, responsive to a selection of anetwork device, some or all data related to the network device may bedepicted by the visualization engine 123. In another example, responsiveto a selection of an existing wireless device, the visualization engine123 may depict information about the wireless environment in the sectionof the map being viewed. In some of these examples, responsive toselection of a new network device, the visualization engine 123 mayenable options for purchase of the new network device or devices similarto the new network device. For example, the visualization engine 123 mayprovide a marketplace for network devices that could be used in thesmart city. The types of information displayed by the visualizationengine 123 in response to the selection of types of data points (e.g.,devices, lidar data, imagery data, city infrastructure, wireless signaldata, lidar point cloud, radio frequency data, and/or other types ofdata), is not limited to the examples described herein.

In some examples, the visualization engine 123 may enable the placementand/or movement of a network device in a section of the map beingviewed. Response to receiving an input to change information related toa placement of a first network device of the set of network devices inthe section of the map being viewed, the visualization engine 123 maycause the network device placement engine 122 to re-determine theplacement of the set of network devices based upon the new placement ofthe first network device, in a manner the same as or similar to thatdescribed above. The visualization engine 123 may then provide anupdated visualization of the re-determined placement of the set ofnetwork devices, including the new placement of the first networkdevice. As such, the visualization engine 123 (and smart city managementsystem 110) may provide a recommendation for placement of a set ofnetwork devices but may also enable customization by a user of thesystem of a sub-set or all of the network devices, and may update therecommendation based on the received user customization(s).

In some examples, the visualization engine 123 may enable interactionwith the map in an augmented and/or virtual reality environment. Thevisualization engine 123 may enable customization of the placement ofthe network devices, interaction with the data in the section of the mapbeing viewed, the display of additional information related to selecteddata in the section of the map being viewed, and/or other interactionswith the map in the augmented and/or virtual reality environment aswell.

FIGS. 2A-2D, which will be described in further detail below, showvisualizations of various locations overlaid with various types of datafrom the smart city management system 110, and depict how the overlaiddata can be used for identification, planning, and management purposes,among others. The visualizations available via the smart city managementsystem 110 and the visualization engine 123 described below are notlimited to the visualizations depicted in FIGS. 2A-2D.

In FIG. 2A, one example of visualization 200 of the data from the smartcity management system 110 in a location is depicted. In the exampleillustrated in FIG. 2A, the radio frequency data may be represented byan overlay box 202 over the lidar point cloud 201. The overlay mayinclude further information about wireless data included in the radiofrequency data. Example of the type of wireless data shown in overlaybox 202 is not limited to the type of radio frequency data that could beincluded in the overlay box 202. In some examples, the data shown in theoverlay box 202 may be a predetermined set of data, may be determinedfrom a user request to the visualization engine 123, may be machinelearned from the smart city management system 110, and/or may otherwisebe determined.

In FIG. 2B, another example of visualization 210 of the data from thesmart city management system 110 in a location is depicted. In theexample illustrated in FIG. 2B, the radio frequency data may berepresented by a set of graphical elements 211, the lidar data may berepresented by a lidar point cloud 212, and the imagery data 213 may berepresented by imagery included in the visualization 210.

In FIG. 2C, another example of visualization 220 of the data from thesmart city management system 110 in a location is depicted. In theexample illustrated in FIG. 2C, the imagery data for the location may bedepicted, along with city infrastructure being represented by graphicalelements 222, 223, and potential existing network devices beingrepresented by graphical elements 221 included in the visualization 220.

In FIG. 2D, another example of visualization 230 of the data from thesmart city management system 110 in a location is depicted. In theexample illustrated in FIG. 2D, different types of wireless signals maybe represented by graphical elements 231, 232, 233 included in thevisualization 230.

Returning to FIG. 1, the visualization engine 123 may provide avisualization of the data related to the smart city and/or for alocation in the smart city using other types of depictions of thegeographic area as well, such as a geographical map, topographical map,highway map, photographic map, heat map, and/or other map type depictingof the geographic area. The visualization engine 123 may also providedifferent types of visualizations of the geographic area, including, butnot limited to a lidar point cloud, lidar map, wireless map, colorizedlidar point cloud, and/or other base type of visualization upon whichadditional data may be overlaid. For example, the visualization engine123 may provide a map depicting the physical locations associated withproperties associated with the collected radio frequency data andcollected lidar data. In another example, the visualization engine 123may provide a heat map related to the mapped radio frequency data andmapped lidar data to better depict the relationship between the physicalterrain and signal strengths in the geographic area.

In some examples, the visualization engine 123 may provide one ormultiple map types and/or visualizations related to the mapped data. Insome examples, the visualization engine 123 may enable the changing ofvisualization from one map to another. For example, the visualizationengine may enable the change of a visualization from a lidar point cloudto photographic imagery, may combine the lidar point cloud and thephotographic imagery to enable a colorized lidar point cloud. In anotherexample, the visualization engine 123 may enable changing from acombination of map type and visualization type (e.g., heat map and lidarpoint cloud to geographical map and colorized lidar point cloud). Thetypes of maps and types of visualizations of the mapped radio frequencydata and lidar data are not limited to the examples described herein andcan be changed based on the context of use of the mapped radio frequencydata and lidar data as well.

In some examples, the visualization engine 123 may provide a time-boundvisualization of the depicted data. For example, the visualizationengine 123 may include a time bar or other time indicator that may beselectable or changed. In these examples, the visualization engine mayenable a visualization of the change of the depicted data over time,based on a selection of a specific time or time range via the timebar/time indicator. In some of these examples, the visualization engine123 may provide a special indicator for objects and/or individual datapoints of the depicted data that appear or disappear as the timebar/time indicator selection is changed.

In some examples, smart city management system 110 may collect the radiofrequency data, wireless data, lidar data, and/or other data fromvehicles, scooters, segways, aircraft, drones, and/or othertransportation devices that use wireless signals for data. In theseexamples, smart city management system 100 may use the collected data toimprove the determined placement of network devices in the smart city,and/or the autonomous functionality of these transportation devices,including physical object avoidance and better signal strength andcoverage.

In some examples, smart city management system 110 may provideinformation related to infrastructure (e.g., roads, rest areas, and/orother infrastructure objects) that may be covered by wireless signals toassist in communication and planning related to infrastructure,construction, and/or other transportation related needs.

In some examples, smart city management system 110 may track thewireless devices in a location. The smart city management system 110 maytrack the devices for traffic usage, to determine the types of devicesthat are being used, to track the efficiency of the placement ofexisting network devices, update the placement of network devices in thelocation, and/or to otherwise optimize the placement of network devicesin the smart city.

In some examples, smart city management system 110 may determine whetherrisks exist in a geographic location (e.g., a secure area, a governmentproperty, an isolated corporate property, etc.) based on the data usedby the smart city management system 110. For example, the smart citymanagement system 110 may provide an alert if an unauthorized networkdevice is found in the geographic location, if a wireless signal hascharacteristics that change more than a predetermined threshold over aperiod of time, if signal obstruction occurs, if the usage of wirelessdevices in the geographic area indicate an abnormality, and/or if otheractivities that may be determined to be risks occur.

In some examples, the smart city management system 110 may analyze thedata related to the smart city to monitor conditions related to naturaldisasters, critical drainage infrastructure, and/or other municipalemergencies. In other examples, smart city management system 110 mayanalyze the data related to the smart city to monitor conditions withfactories, plants, data centers, land-based oil pipelines, oil drillingplatforms, solar wind farms, vending machines, payment processingmachines, and/or for other corporate uses. In some examples, the smartcity management system 100 may predict new deployments of networkdevices and/or other equipment related to the analysis of the datarelated to the smart city.

In some examples, smart city management system 110 may analyze the datarelated to the smart city to increase public safety. For example, thesmart city management system 100 may provide alerts and/or predictionsrelated to coverage using collected data related to police dashboard,body cam, and/or other police related, network enabled equipment.Similarly, the smart city management system 110 may enhance coverage forfire departments based on sensors available with ambulances and reduceresponse time based on the mapped radio frequency data and lidar data.

In performing their respective functions, engines 121-123 may accessdata storage 129 and/or other suitable database(s). Data storage 129 mayrepresent any memory accessible to smart city management system 110 thatcan be used to store and retrieve data. Data storage 129 and/or otherdatabase may comprise random access memory (RAM), read-only memory(ROM), electrically-erasable programmable read-only memory (EEPROM),cache memory, floppy disks, hard disks, optical disks, tapes, solidstate drives, flash drives, portable compact disks, and/or other storagemedia for storing computer-executable instructions and/or data. Smartcity management system 110 may access data storage 129 locally orremotely via network 50 or other networks.

Data storage 129 may include a database to organize and store data. Thedatabase may reside in a single or multiple physical device(s) and in asingle or multiple physical location(s). The database may store aplurality of types of data and/or files and associated data or filedescription, administrative information, or any other data.

FIGS. 2A-2D are diagrams depicting an example user interface 200 formanaging a smart city. FIGS. 2A-2D are described herein with respect toFIG. 1.

FIG. 3 is a block diagram depicting an example machine-readable storagemedium 310 comprising instructions executable by a processor formanaging a smart city.

In the foregoing discussion, engines 121-123 were described ascombinations of hardware and programming. Engines 121-123 may beimplemented in a number of fashions. Referring to FIG. 3, theprogramming may be processor executable instructions 321-323 stored on amachine-readable storage medium 310 and the hardware may include aprocessor 311 for executing those instructions. Thus, machine-readablestorage medium 310 can be said to store program instructions or codethat when executed by processor 311 implements smart city managementsystem 110 of FIG. 1.

In FIG. 3, the executable program instructions in machine-readablestorage medium 310 are depicted as smart city data managementinstructions 321, network device placement instructions 322, andvisualization instructions 323. Instructions 321-323 represent programinstructions that, when executed, cause processor 311 to implementengines 121-123, respectively.

Machine-readable storage medium 310 may be any electronic, magnetic,optical, or other physical storage device that contains or storesexecutable instructions. In some implementations, machine-readablestorage medium 310 may be a non-transitory storage medium, where theterm “non-transitory” does not encompass transitory propagating signals.Machine-readable storage medium 310 may be implemented in a singledevice or distributed across devices. Likewise, processor 311 mayrepresent any number of processors capable of executing instructionsstored by machine-readable storage medium 310. Processor 311 may beintegrated in a single device or distributed across devices. Further,machine-readable storage medium 310 may be fully or partially integratedin the same device as processor 311, or it may be separate butaccessible to that device and processor 311.

In one example, the program instructions may be part of an installationpackage that when installed can be executed by processor 311 toimplement smart city management system 110. In this case,machine-readable storage medium 310 may be a portable medium such as afloppy disk, CD, DVD, or flash drive or a memory maintained by a serverfrom which the installation package can be downloaded and installed. Inanother example, the program instructions may be part of an applicationor applications already installed. Here, machine-readable storage medium310 may include a hard disk, optical disk, tapes, solid state drives,RAM, ROM, EEPROM, or the like.

Processor 311 may be at least one central processing unit (CPU),microprocessor, and/or other hardware device suitable for retrieval andexecution of instructions stored in machine-readable storage medium 310.Processor 311 may fetch, decode, and execute program instructions321-323, and/or other instructions. As an alternative or in addition toretrieving and executing instructions, processor 311 may include atleast one electronic circuit comprising a number of electroniccomponents for performing the functionality of at least one ofinstructions 321-323, and/or other instructions.

FIG. 4 is a flow diagram depicting an example method 400 for managing asmart city. The various processing blocks and/or data flows depicted inFIG. 4 (and in the other drawing figures described herein) are describedin greater detail herein. The described processing blocks may beaccomplished using some or all of the system components described indetail above and, in some implementations, various processing blocks maybe performed in different sequences and various processing blocks may beomitted. Additional processing blocks may be performed along with someor all of the processing blocks shown in the depicted flow diagrams.Some processing blocks may be performed simultaneously. Accordingly,method 400 as illustrated (and described in greater detail below) ismeant to be an example and, as such, should not be viewed as limiting.Method 400 may be implemented in the form of executable instructionsstored on a machine-readable storage medium, such as storage medium 310,and/or in the form of electronic circuitry.

In block 421, method 400 may include creating a digital twin of thesmart city based on mapping lidar data for the smart city and radiofrequency data for the smart city. Referring to FIG. 1, smart city datamanagement engine 121 may be responsible for implementing block 421.

In block 422, method 400 may include determining placement of a set ofnetwork devices in the smart city based on the created digital twin.Referring to FIG. 1, network device placement engine 122 may beresponsible for implementing block 422.

In block 423, method 400 may include providing a visualization of thedetermined placement of the set of network devices. Referring to FIG. 1,visualization engine 123 may be responsible for implementing block 423.

FIG. 5 is a flow diagram depicting an example method 500 for managing asmart city. The various processing blocks and/or data flows depicted inFIG. 5 (and in the other drawing figures described herein) are describedin greater detail herein. The described processing blocks may beaccomplished using some or all of the system components described indetail above and, in some implementations, various processing blocks maybe performed in different sequences and various processing blocks may beomitted. Additional processing blocks may be performed along with someor all of the processing blocks shown in the depicted flow diagrams.Some processing blocks may be performed simultaneously. Accordingly,method 500 as illustrated (and described in greater detail below) ismeant to be an example and, as such, should not be viewed as limiting.Method 500 may be implemented in the form of executable instructionsstored on a machine-readable storage medium, such as storage medium 310,and/or in the form of electronic circuitry.

In block 521, method 500 may include mapping lidar data and radiofrequency data for the smart city. Referring to FIG. 1, smart city datamanagement engine 121 may be responsible for implementing block 521.

In block 522, method 500 may include identifying a set of existingnetwork devices in the smart city based on the mapped lidar and radiofrequency data. Referring to FIG. 1, network device placement engine 122may be responsible for implementing block 422.

In block 523, method 500 may include determining placement of a set ofnetwork devices in the smart city, where the set of network devicescomprises the set of existing network devices and a set of new networkdevices. Referring to FIG. 1, network device placement engine 122 may beresponsible for implementing block 523.

In block 524, method 500 may include providing a recommendation of thedetermined placement of the set of network devices. Referring to FIG. 1,visualization engine 123 may be responsible for implementing block 524.

The foregoing disclosure describes a number of example implementationsfor managing a smart city. The disclosed examples may include systems,devices, computer-readable storage media, and methods for managing asmart city. For purposes of explanation, certain examples are describedwith reference to the components illustrated in FIGS. 1-5. Thefunctionality of the illustrated components may overlap, however, andmay be present in a fewer or greater number of elements and components.

Further, all or part of the functionality of illustrated elements mayco-exist or be distributed among several geographically dispersedlocations. Moreover, the disclosed examples may be implemented invarious environments and are not limited to the illustrated examples.Further, the sequence of operations described in connection with FIGS. 4and 5 are examples and are not intended to be limiting. Additional orfewer operations or combinations of operations may be used or may varywithout departing from the scope of the disclosed examples. Furthermore,implementations consistent with the disclosed examples need not performthe sequence of operations in any particular order. Thus, the presentdisclosure merely sets forth possible examples of implementations, andmany variations and modifications may be made to the described examples.All such modifications and variations are intended to be included withinthe scope of this disclosure and protected by the following claims.

What is claimed is:
 1. A computer-implemented method for managingdeployment of a network in a smart city, the method being implemented bya physical processor implementing machine readable instructions, themethod comprising: creating a digital twin of the smart city based onmapping lidar data for the smart city and radio frequency data for thesmart city, the digital twin comprising the mapped lidar data and themapped radio frequency data for the smart city; de-conflicting signalsfrom a set of existing network devices in the smart city, the set ofexisting network devices identified based on the created digital twin;determining placement of a set of new network devices in the smart citybased on the set of existing network devices and the de-conflictedsignals from the set of existing network devices; and providing avisualization of the placement of the set of existing network devicesand the set of new network devices in the smart city.
 2. Thecomputer-implemented method of claim 1, further comprising: identifyinga gap in a spectrum of frequencies used by the set of existing networkdevices; identifying available unused frequencies based on theidentified gap; determining the placement of the set of new networkdevices based on the identified gap and identified unused frequencies.3. The computer-implemented method of claim 2, wherein determining theplacement of the set of new network devices comprises: determining afirst network device from the set of new network devices to place in thesmart city based on the first network device being associated with anidentified available unused frequency from the identified availableunused frequencies; and determining placement of the first networkdevice based on a physical terrain of the smart city and the mappedlidar and radio frequency data.
 4. The computer-implemented method ofclaim 1, wherein determining the placement of the set of new networkdevices comprises: determining the identified set of new network devicesby de-conflicting the set of new network devices with the set ofexisting network devices.
 5. The computer-implemented method of claim 4,wherein determining the placement of the set of new network devicescomprises: determining a first network device from the set of newnetwork devices to place in the smart city based on a first set ofcapabilities associated with the first network device; and determiningplacement of the first network device based on a physical terrain of thesmart city and the mapped lidar and radio frequency data.
 6. Thecomputer-implemented method of claim 4, wherein determining theplacement of the set of new network devices comprises: for a first zonein a set of zones in the smart city, analyzing information related to aterrain of the first zone; analyzing a wireless environment of the firstzone; and determining placement of the set of new network devices in thefirst zone based on the analysis of the information related to theterrain and the analysis of the wireless environment.
 7. Thecomputer-implemented method of claim 1, wherein determining theplacement of the set of new network devices comprises: receivinginformation about a required set of devices in a first zone in a set ofzones in the smart city; and wherein providing the visualizationcomprises: providing a visualization of the first zone with arecommended placement of each of the required set of devices.
 8. Thecomputer-implemented method of claim 1, wherein providing thevisualization comprises: providing an interactive map of the determinedplacement of the set of existing network devices and the set of newnetwork devices in the smart city; receiving input to change informationrelated to a first placement of a first device of the set of new networkdevices; re-determining placement of the set of new network devicesbased on the received input; and providing an updated visualization ofthe re-determined placement of the set of existing network devices andthe set of new network devices, including a second placement of thefirst device in the smart city.
 9. A system for managing deployment of anetwork in a smart city, the system comprising a physical processorexecuting machine readable instructions that cause the system to: createa digital twin of the smart city based on mapping lidar data for thesmart city and radio frequency data for the smart city, the digital twincomprising the mapped lidar data and the mapped radio frequency data forthe smart city; de-conflict signals from a set of existing networkdevices in the smart city, the set of existing network devicesidentified based on the created digital twin; determine placement of aset of new network devices in the smart city based on the set ofexisting network devices and the de-conflicted signals from the set ofexisting network devices; and provide a visualization of the placementof the set of existing network devices and the set of new networkdevices in the smart city.
 10. The system of claim 9, the physicalprocessor further executing machine readable instructions that cause thesystem to: identify a gap in a spectrum of frequencies used by the setof existing network devices; identify available unused frequencies basedon the identified gap; determine the placement of the set of new networkdevices based on the identified gap and identified unused frequencies.11. The system of claim 10, wherein determining the placement of the setof new network devices comprises: determine a first network device fromthe set of new network devices to place in the smart city based on thefirst network device being associated with an identified availableunused frequency from the identified available unused frequencies; anddetermine placement of the first network device based on a physicalterrain of the smart city and the mapped lidar and radio frequency data.12. The system of claim 11, wherein determining the placement of the setof new network devices comprises: determining the first network devicefrom the set of new network devices to place in the smart city based ona first set of capabilities associated with the first network device.13. The system of claim 10, wherein determining the placement of the setof new network devices comprises: for a first zone in a set of zones inthe smart city, analyzing information related to a terrain of the firstzone; analyzing a wireless environment of the first zone; anddetermining placement of the set of new network devices in the firstzone based on the analysis of the information related to the terrain andthe analysis of the wireless environment.
 14. The system of claim 9,wherein providing the visualization comprises: providing an interactivemap of the determined placement of the set of existing network devicesand the set of new network devices in the smart city; receiving input tochange information related to a first placement of a first device of theset of new network devices; re-determining placement of the set of newnetwork devices based on the received input; and providing an updatedvisualization of the re-determined placement of the set of existingnetwork devices and the set of new network devices, including a secondplacement of the first device in the smart city.
 15. A non-transitorymachine-readable storage medium comprising instructions executable by aphysical processor of a computing device for managing deployment of anetwork in a smart city, the machine-readable storage medium comprising:instructions to create a digital twin of the smart city based on mappinglidar data for the smart city and radio frequency data for the smartcity, the digital twin comprising the mapped lidar data and the mappedradio frequency data for the smart city; de-conflict signals from a setof existing network devices in the smart city, the set of existingnetwork devices identified based on the created digital twin; determineplacement of a set of new network devices in the smart city based on theset of existing network devices and the de-conflicted signals from theset of existing network devices; and provide a visualization of theplacement of the set of existing network devices and the set of newnetwork devices in the smart city.
 16. The non-transitorymachine-readable storage medium of claim 15, the physical furthercomprising machine readable instructions to: identify a gap in aspectrum of frequencies used by the set of existing network devices;identify available unused frequencies based on the identified gap;determine the placement of the set of new network devices based on theidentified gap and identified unused frequencies.
 17. The non-transitorymachine-readable storage medium of claim 16, wherein determining theplacement of the set of new network devices comprises: determine a firstnetwork device from the set of new network devices to place in the smartcity based on the first network device being associated with anidentified available unused frequency from the identified availableunused frequencies; and determine placement of the first network devicebased on a physical terrain of the smart city and the mapped lidar andradio frequency data.
 18. The non-transitory machine-readable storagemedium of claim 17, wherein determining the placement of the set of newnetwork devices comprises: determining the first network device from theset of new network devices to place in the smart city based on a firstset of capabilities associated with the first network device.
 19. Thenon-transitory machine-readable storage medium of claim 16, whereindetermining the placement of the set of new network devices comprises:for a first zone in a set of zones in the smart city, analyzinginformation related to a terrain of the first zone; analyzing a wirelessenvironment of the first zone; and determining placement of the set ofnew network devices in the first zone based on the analysis of theinformation related to the terrain and the analysis of the wirelessenvironment.
 20. The non-transitory machine-readable storage medium ofclaim 1, wherein providing the visualization comprises: providing aninteractive map of the determined placement of the set of existingnetwork devices and the set of new network devices in the smart city;receiving input to change information related to a first placement of afirst device of the set of new network devices; re-determining placementof the set of new network devices based on the received input; andproviding an updated visualization of the re-determined placement of theset of existing network devices and the set of new network devices,including a second placement of the first device in the smart city.